Now a lot of robotics is very impressive, but manipulation robotics is really just in the dark ages.  如今许多项机器人技术已经相当厉害，可是在机器人动作控制方面我们遇到了瓶颈，看不到曙光。

　　So this is the end of a Ph.D. project from one of the best robotics institutes.  这是在一个顶尖机器人控制学院里，一个博士研究项目的成果。

　　And the student has trained this robot to pour this water into a glass.  这位博士生训练这个机器人向这个玻璃杯里面倒水。

　　It's a hard problem because the water sloshes about, but it can do it.  这很难实现，因为水会洒出来，不过机器人能做到。

　　But it doesn't do it with anything like the agility of a human.  但是机器人无法达到任何接近于人类的灵活程度。

　　Now if you want this robot to do a different task, that's another three-year Ph.D. program.  并且如果想让机器人完成另一个任务的话，就得要另外一个历时三年的博士项目了。

　　There is no generalization at all from one task to another in robotics. 在机器人控制领域，从一项任务到另一项任务，根本没有共通性可言。

　　Now we can compare this to cutting-edge human performance.  现在我们再来与最尖端的人类性能做比较。

　　So what I'm going to show you is Emily Fox winning the world record for cup stacking.  我会播放艾米莉·福克斯叠杯子获得世界记录的场景。

　　Now the Americans in the audience will know all about cup stacking.  观众里有美国人的话一定知道叠杯子这个游戏。

　　It's a high school sport where you have 12 cups you have to stack and unstack against the clock in a prescribed order.  这是个高中里的娱乐项目，要求把12只杯子按照一定顺序以最快速度摞起来再展开回来。

　　And this is her getting the world record in real time.   下面就是她创下世界记录的实时情况。

　　And she's pretty happy. We have no idea what is going on inside her brain when she does that, and that's what we'd like to know. 看她多开心。我们不清楚她叠杯子的时候大脑里发生了什么，而且我们的确很想知道。

　　So in my group, what we try to do is reverse engineer how humans control movement.  所以我和我的团队，试图去逆向实现人类如何控制动作的过程。

　　And it sounds like an easy problem.  看起来问题很简单。

　　You send a command down, it causes muscles to contract.  你送出一个指令，令肌肉收缩。

　　Your arm or body moves, and you get sensory feedback from vision, from skin, from muscles and so on.  这样手臂或躯干就会运动，然后能够获得从视觉、皮肤和肌肉和其他地方获得感官回馈。

　　The trouble is these signals are not the beautiful signals you want them to be.  但问题是这些信号远没有想象中的那样完美。

　　So one thing that makes controlling movement difficult is, for example, sensory feedback is extremely noisy.  比如说，其中的难点之一就是感官回馈中的杂音非常大。

　　Now by noise, I do not mean sound.  我这里所说的“杂音”不是指声音。

　　We use it in the engineering and neuroscience sense meaning a random noise corrupting a signal.  在工程学和神经学里面，我们用“杂音”来表示干扰正常信号的随机杂乱信号。

　　So the old days before digital radio when you were tuning in your radio and you heard "crrcckkk" on the station you wanted to hear, that was the noise.  所以以前还没有电子收音机的时候，如果调那种老式收音机，电台里面有时候有“喀喇喀喇”的声音，那个就是杂音。

　　But more generally, this noise is something that corrupts the signal. 推广来说，杂音就是相对于正常信号的干扰信号。